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Improving Direct Marketing ROI through Advanced Predictive Modeling

Predictive modeling has been widely adopted in the direct marketing industry. So much so, it’s become the direct marketer’s “best friend.”

Although complex statistical algorithms are used “behind the scenes” in developing predictive modeling applications, the overall concept and primary goal – to find customers or prospects who are most likely to exhibit the desired behavior – is simple. In the context of new customer acquisition, the goal is to find prospects that will respond to a direct marketing offer and subsequently become a long-term customer.

Direct marketers are maximizing the ROI for their programs by using model-driven targeting strategies. In one case, a media industry marketer was able to generate annual savings more than $4 MM by targeting the right people.

Here are a few tips to get you started:

Use segmented modeling techniques. Predictive powers of models can be improved by using segmented modeling approaches. Creation of segments for modeling purposes can be done using quantitative methods, such as tree-based algorithms. Often, different demographic and economic factors drive response and conversion behaviors for unique prospect segments. Segmentation approaches can differ greatly by industry and by the specific client situation.

For example, in the financial services industry, marketers can typically boost the power of their targeting models by developing separate modeling algorithms for different credit segments. It’s common to find that the response drivers for high-credit quality segments are different than the response drivers for high-risk segments. By the same token, separate models by state or geography have been proven to generate better results in the auto insurance industry

Make continuous improvements. Predictive modeling is never a one-shot process. Direct marketers can improve the predictive performance of models by continuously incorporating the results from their most recent direct marketing campaign and response activity.

There are several reasons why model updates lead to better marketing results. First, consumer response dynamics change constantly, especially within highly competitive and fast-moving markets, such as credit card, auto insurance, and retail. Second, richer response data becomes available as direct marketers launch more campaigns, and more data typically leads to better targeting models. And finally, marketers can, over time, perform tests to randomly selected audiences, which could create an “unbiased” pool of responders and converters that they can use to develop better models.

Continuous modeling improvements can boost results for direct marketers across many industries. In some cases, they can improve response rates in the 15% to 25% range over baseline models.

Maximize available data for modeling. Richer data typically translates into better predictive models. One data category that tends to make significant industry- and client-specific contributions to predictive power is derived data. For example, the distance to a store location is a strong driver for companies with retail locations and can be calculated by taking the longitude and latitude data into account.

Additionally, summarized promotion history data, such as the number of prior contacts over the past year, can also be a strong contributor to the model’s predictive power. Geo-targeting indexes, such as customer or responder penetrations, can also lead to significant improvements.

Client-specific derived data can be combined with third-party sources to create a rich pool of valuable customer data. There is a wealth of third-party demographic, psychographic, financial, and industry-specific data available, and more valuable data is coming to the marketplace that marketers can tap into to boost the performance of their targeting models.

Direct marketers can use advanced predictive modeling techniques to gain a competitive advantage and boost their marketing ROI. Advanced modeling is a big part of audience selection and can drive greater levels of revenue and profits – especially in competitive environments. Through continuous improvements in predictive modeling, marketers can achieve even aggressive goals and deliver the financial returns expected.

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